Related papers: Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications

Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications

URL: http://arxiv.org/abs/2209.14975v1
Date: Thu, 29 Sep 2022 17:44:14 GMT
Title: Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications
Authors: Junda Wang, Weijian Li, Han Wang, Hanjia Lyu, Caroline Thirukumaran, Addisu Mesfin, Jiebo Luo
Abstract summary: We introduce a novel method with a variable decorrelation regularizer to handle both linear and nonlinear confounding. We employ association rules as new representations using association rule mining based on the original features to increase model interpretability.
Score: 60.26261850082012
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Causal inference and model interpretability research are gaining increasing attention, especially in the domains of healthcare and bioinformatics. Despite recent successes in this field, decorrelating features under nonlinear environments with human interpretable representations has not been adequately investigated. To address this issue, we introduce a novel method with a variable decorrelation regularizer to handle both linear and nonlinear confounding. Moreover, we employ association rules as new representations using association rule mining based on the original features to further proximate human decision patterns to increase model interpretability. Extensive experiments are conducted on four healthcare datasets (one synthetically generated and three real-world collections on different diseases). Quantitative results in comparison to baseline approaches on parameter estimation and causality computation indicate the model's superior performance. Furthermore, expert evaluation given by healthcare professionals validates the effectiveness and interpretability of the proposed model.

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